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On-chip vs. off-chip memory: the data partitioning problem in embedded processor-based systems
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Source ACM Transactions on Design Automation of Electronic Systems (TODAES) archive
Volume 5 ,  Issue 3  (July 2000) table of contents
Pages: 682 - 704  
Year of Publication: 2000
ISSN:1084-4309
Authors
Preeti Ranjan Panda  Synopsys, Inc., Mountain View, CA
Nikil D. Dutt  Univ. of California at Irvine, Irvine
Alexandru Nicolau  Univ. of California at Irvine, Irvine
Publisher
ACM  New York, NY, USA
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ABSTRACT

Efficient utilization of on-chip memory space is extremely important in modern embedded system applications based on processor cores. In addition to a data cache that interfaces with slower off-chip memory, a fast on-chip SRAM, called Scratch-Pad memory, is often used in several applications, so that critical data can be stored there with a guaranteed fast access time. We present a technique for efficiently exploiting on-chip Scratch-Pad memory by partitioning the application's scalar and arrayed variables into off-chip DRAM and on-chip Scratch-Pad SRAM, with the goal of minimizing the total execution time of embedded applications. We also present extensions of our proposed memory assignment strategy to handle context switching between multiple programs, as well as a generalized memory hierarchy. Our experiments on code kernels from typical applications show that our technique results in significant performance improvements.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  28


REVIEW

"Neil Robert Karl : Reviewer"

An algorithm is outlined that allocates program variables to fast on-chip static random access memory (SRAM) in order to optimize program CPU execution time. The algorithm needs to be implemented in a precompiler for a target langu  more...

Collaborative Colleagues:
Preeti Ranjan Panda: colleagues
Nikil D. Dutt: colleagues
Alexandru Nicolau: colleagues